US 12,354,326 B2
Image recognition apparatus, image recognition method, and a learning data set generation apparatus
Yasutaka Okada, Kobe (JP); Ryusuke Seki, Kobe (JP); and Yuki Katayama, Kobe (JP)
Assigned to DENSO TEN Limited, Kobe (JP)
Filed by DENSO TEN Limited, Kobe (JP)
Filed on Aug. 23, 2022, as Appl. No. 17/893,792.
Claims priority of application No. 2022-015322 (JP), filed on Feb. 3, 2022; application No. 2022-015323 (JP), filed on Feb. 3, 2022; and application No. 2022-015325 (JP), filed on Feb. 3, 2022.
Prior Publication US 2023/0245422 A1, Aug. 3, 2023
Int. Cl. G06V 10/764 (2022.01); G06V 10/22 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01)
CPC G06V 10/764 (2022.01) [G06V 10/22 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01)] 12 Claims
OG exemplary drawing
 
1. An image recognition apparatus comprising a processor configured to make an inference to recognize a recognition target object in an input image, wherein
the processor is configured to
perform, based on the input image and using a plurality of mutually different image recognition models, a plurality of object detection processes to detect as an object detection region a region in the input image where the recognition target object is judged to be present, and
generate inference result data indicating a result of the inference in accordance with a degree of overlap among a plurality of object detection regions detected in the plurality of object detection processes,
the processor is configured to generate, in each object detection process, object detection data including
class data indicating a kind of an object in the object detection region,
position/shape data of the object detection region, and
a reliability of the result of the object detection process,
each object detection process is a single-class object detection process dealing with a single kind of object as the recognition target object, the recognition target objects of the plurality of object detection processes being of a same kind, and
the processor is configured to
identify a maximum reliability among a plurality of reliabilities derived in the plurality of object detection processes and
set to the inference result data the class data and the position/shape data corresponding to the maximum reliability if the degree of overlap is equal to or higher than a predetermined overlap threshold value.